code: def init_back_translation_model(
      from_model_name,
      to_model_name,
      device,
      force_reload=False,
      batch_size=32,
      max_length=None,
  ):
      global BACK_TRANSLATION_MODELS
  
      model_name = "_".join(
          [from_model_name, to_model_name, str(device)]
      )
      if model_name in BACK_TRANSLATION_MODELS and not force_reload:
          BACK_TRANSLATION_MODELS[model_name].batch_size = batch_size
          BACK_TRANSLATION_MODELS[model_name].max_length = max_length
  
          return BACK_TRANSLATION_MODELS[model_name]
  
      model = nml.MtTransformers(
          src_model_name=from_model_name,
          tgt_model_name=to_model_name,
          device=device,
          batch_size=batch_size,
          max_length=max_length,
      )
  
      BACK_TRANSLATION_MODELS[model_name] = model
      return model
  
file_path: nlpaug/nlpaug/augmenter/word/back_translation.py
imports: import string
  import os
  from nlpaug.augmenter.word import WordAugmenter
  import nlpaug.model.lang_models
label_desc: Use image node_id 1 for calling a global function with example usage: init_back_translation_model(from_model_name, to_model_name, device, force_reload, batch_size, max_length) and returns: model, BACK_TRANSLATION_MODELS